Low-efficiency land use identification method and device based on pressure-state-response framework

Abstract

The invention discloses a low-efficiency land use identification method and device based on a pressure-state-response framework, and the method comprises the steps: obtaining the geographic position data, land use type data, social and economic data, population data, ecological environment data, infrastructure data and regulation and control response data of a plurality of target regions; calculating a spatial correlation coefficient between the first target area and the second target area based on the geographic position data, the land use type data, the social economic data and the ecological environment data to obtain a spatial correlation matrix; based on the social economic data, the population data, the ecological environment data, the infrastructure data, the regulation and control response data and the spatial incidence matrix, constructing a low-efficiency land use evaluation index system; determining a comprehensive evaluation index set of each target area through the low-efficiency land use evaluation index system and the land use type data; and determining the low-efficiency land use level of each target area based on the comprehensive evaluation index set, and obtaining a low-efficiency land use identification result. According to the invention, the accuracy of low-efficiency land use identification can be improved.

Description

Technical Field

[0001] This application relates to the field of identification of inefficient land use, and specifically relates to a method and device for identifying inefficient land use based on the Pressure-State-Response (PSR) framework. Background Art

[0002] With the acceleration of the urbanization process and the continuous increase in the intensity of national territorial space development, the problem of inefficient land use has become increasingly prominent. Inefficient land use not only causes waste of land resources, but also affects regional coordinated development and restricts the high-quality development of cities. Therefore, scientifically identifying inefficient land use is of great significance for improving land use efficiency and promoting regional coordinated development.

[0003] In the prior art, the method for identifying inefficient land use based on the Pressure-State-Response (PSR) framework evaluates land use efficiency from multiple dimensions by establishing an index system that reflects land use pressure, land use status, and management response.

[0004] However, the existing method for identifying inefficient land use based on the PSR framework regards the target areas as independent evaluation objects during the process of identifying inefficient land use. This evaluation method cannot reflect the mutual influence between target areas, resulting in a deviation between the evaluation result and the actual development situation, and reducing the accuracy of identifying inefficient land use. Summary of the Invention

[0005] This application provides a method and device for identifying inefficient land use based on the Pressure-State-Response framework, which is used to improve the accuracy of identifying inefficient land use.

[0006] In the first aspect of this application, a method for identifying inefficient land use based on the Pressure-State-Response framework is provided, which is applied to a server. The method includes: Obtain the geographical location data, land use type data, social and economic data, population data, ecological environment data, infrastructure data, and regulatory response data of multiple target areas; calculate the spatial correlation coefficient between the first target area and the second target area based on the geographical location data, land use type data, social and economic data, and ecological environment data to obtain a spatial correlation matrix, where the first target area is any target area, and the second target area is any target area other than the first target area; construct an evaluation index system for inefficient land use based on the social and economic data, population data, ecological environment data, infrastructure data, regulatory response data, and spatial correlation matrix; determine the comprehensive evaluation index set of each target area through the evaluation index system for inefficient land use and the land use type data; determine the inefficient land use level of each target area based on the comprehensive evaluation index set to obtain the identification result of inefficient land use.

[0007] Optionally, calculate the spatial correlation coefficient between the first target area and the second target area based on geographical location data, land use type data, socio-economic data, and ecological environment data to obtain a spatial correlation matrix, specifically including: Determine the first central coordinate point of the first target area and the second central coordinate point of the second target area according to the geographical location data; calculate the geographical distance attenuation coefficient between the first target area and the second target area based on the spatial distance between the first central coordinate point and the second central coordinate point; construct a land use type conversion matrix based on the land use type data, and calculate the land use type similarity between the first target area and the second target area according to the land use type conversion matrix; calculate the socio-ecological system coupling degree between the first target area and the second target area based on the socio-economic data and the ecological environment data; perform weighted calculation on the geographical distance attenuation coefficient, the land use type similarity, and the socio-ecological system coupling degree to obtain the spatial correlation coefficient between the first target area and the second target area; construct a spatial correlation matrix according to the spatial correlation coefficient.

[0008] Optionally, construct an evaluation index system for inefficient land use based on socio-economic data, population data, ecological environment data, infrastructure data, regulation response data, and the spatial correlation matrix, specifically including: Construct pressure layer indicators based on socio-economic data and population data; construct state layer indicators based on ecological environment data and infrastructure data; construct response layer indicators based on regulation response data; determine the first spatial weight of the pressure layer indicators, the second spatial weight of the state layer indicators, and the third spatial weight corresponding to the response layer indicators based on the spatial correlation matrix; construct an evaluation index system for inefficient land use based on the pressure layer indicators, the state layer indicators, the response layer indicators, and the target spatial weights, where the target spatial weights include the first spatial weight, the second spatial weight, and the third spatial weight.

[0009] Optionally, determine the comprehensive evaluation index set of each target area through the evaluation index system for inefficient land use and the land use type data, specifically including: Determine the target index set of each target area through the evaluation index system for inefficient land use, where the target index set includes a pressure index subset, a state index subset, and a response index subset; determine the first index set weight of the pressure index subset, the second index set weight of the state index subset, and the third index set weight corresponding to the response index subset according to the land use type data; obtain the comprehensive evaluation index set of each target area according to the target index set and the target index set weights, where the target index set weights include the first index set weight, the second index set weight, and the third index set weight.

[0010] Optionally, determine the first index set weight of the pressure index subset, the second index set weight of the state index subset, and the third index set weight corresponding to the response index subset according to the land use type data, specifically including: Extract the land use type characteristics of each target area from the land use type data; construct the land use type characteristic matrix of each target area based on the land use type characteristics; determine the first index set weight, the second index set weight, and the third index set weight of the target index set in each target area according to the land use type characteristic matrix.

[0011] Optionally, after determining the inefficient land use level of each target area based on the comprehensive evaluation index set to obtain the inefficient land use identification result, the method further includes: Generate an inefficient land use spatial distribution map according to the inefficient land use identification result in combination with the geographical location data; calculate the plot ratio and building density of each target area based on the comprehensive evaluation index set and the spatial distribution map; evaluate the potential for improving the land use efficiency of each target area according to the plot ratio and building density; generate a land use efficiency improvement plan for each target area based on the potential for improving the land use efficiency.

[0012] Optionally, an inefficient land use identification method based on the pressure-state-response framework, applied to a server, the method further includes: Obtain the target comprehensive evaluation index set of the third target area, where the third target area is a target area with an inefficient land use identification result of inefficient land use, and the target comprehensive evaluation index set includes a target pressure index subset, a target state pressure index subset, and a target response index subset; calculate the influence relationship coefficient between the target pressure index subset and the target state index subset; calculate the driving relationship coefficient between the target state index subset and the target response index subset; when the influence relationship coefficient and the driving relationship coefficient meet the preset warning conditions, trigger the warning mechanism.

[0013] In the second aspect of the present application, an inefficient land use identification system based on the pressure-state-response framework is provided, including: an acquisition module for acquiring geographical location data, land use type data, social and economic data, population data, ecological environment data, infrastructure data, and regulatory response data of multiple target areas; A calculation module for calculating the spatial correlation coefficient between the first target area and the second target area based on the geographical location data, land use type data, social and economic data, and ecological environment data to obtain a spatial correlation matrix, where the first target area is any target area, and the second target area is any target area other than the first target area; A construction module for constructing an inefficient land use evaluation index system based on the social and economic data, population data, ecological environment data, infrastructure data, regulatory response data, and spatial correlation matrix; A determination module for determining the comprehensive evaluation index set of each target area through the inefficient land use evaluation index system and the land use type data; An identification module for determining the inefficient land use level of each target area based on the comprehensive evaluation index set to obtain the inefficient land use identification result.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method as described in any one of the above.

[0015] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the above is executed.

[0016] In summary, one or more technical solutions provided by the present application have at least the following technical effects or advantages: 1. By obtaining multi-dimensional geographical, social, economic, population, and environmental data, a comprehensive data basis for the identification of inefficient land use is established; by calculating the spatial correlation coefficient between target regions to construct a spatial correlation matrix, a quantitative expression of the spatial correlation between regions is realized; on this basis, combined with multi-source data and the spatial correlation matrix, an evaluation index system for inefficient land use is constructed, forming a systematic evaluation framework; by calculating the comprehensive evaluation index through the evaluation index system and determining the level of inefficient land use, the problem of lack of consideration of spatial correlation in traditional inefficient land use identification is solved. This identification mechanism based on the pressure-state-response framework enables the system to improve the accuracy of inefficient land use identification through the comprehensive analysis of multi-dimensional data and the dynamic evaluation of spatial correlation.

[0017] 2. By calculating the geographical distance attenuation coefficient, land use type similarity, and social-ecological system coupling degree of the regional center point, a multi-dimensional spatial correlation evaluation method is established; by performing weighted calculation on these three indicators to obtain the spatial correlation coefficient and construct the correlation matrix, the technical problem that traditional methods only consider a single spatial distance and ignore the functional correlation between regions is solved. This multi-dimensional spatial correlation evaluation mechanism not only improves the accuracy of regional correlation analysis but also realizes the quantitative expression of the functional synergy between regions.

[0018] 3. By constructing a three-layer index system of the pressure layer, state layer, and response layer, a hierarchical framework for the evaluation of inefficient land use is established; based on the spatial correlation matrix, the spatial weights of each layer of indicators are determined and the evaluation index system is constructed, thus solving the technical problems that traditional evaluation methods ignore the spatial correlation and hierarchy between indicators. This multi-level evaluation mechanism based on the PSR framework enables the system to improve the scientificity and reliability of the evaluation results through the dynamic allocation of spatial weights. Description of the Drawings

[0019] Figure 1It is a system architecture diagram related to a method for identifying inefficient land use based on the pressure-state-response framework or a system for identifying inefficient land use based on the pressure-state-response framework in an embodiment of the present application; Figure 2 It is a schematic flowchart of a method for identifying inefficient land use based on the pressure-state-response framework in an embodiment of the present application; Figure 3 It is a schematic structural diagram of a system for identifying inefficient land use based on the pressure-state-response framework in an embodiment of the present application; Figure 4 It is a schematic structural diagram of an electronic device in an embodiment of the present application.

[0020] Explanation of reference numerals: 301, acquisition module; 302, calculation module; 303, construction module; 304, determination module; 305, identification module; 306, optimization module; 307, warning module; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. Detailed implementation manners

[0021] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0022] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0023] In the description of the embodiments of the present application, the meaning of the term "a plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "include but not limited to", unless otherwise specifically emphasized in other ways.

[0024] Figure 1An exemplary system architecture 100 showing an embodiment of an inefficient land use identification method or an inefficient land use identification system based on the pressure - state - response framework to which the present application can be applied is shown.

[0025] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0026] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as model training - type applications, video recognition - type applications, web browser applications, social platform software, etc.

[0027] The terminal devices 101, 102, 103 can be either hardware or software. When the terminal devices 101, 102, 103 are hardware, they can be various electronic devices with a display screen, including but not limited to smart phones, tablet computers, e - book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop portable computers, and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they can be installed in the above - listed electronic devices. It can be implemented as multiple software or software modules (such as multiple software or software modules for providing distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0028] When the terminals 101, 102, 103 are hardware, a video acquisition device can also be installed thereon. The video acquisition device can be various devices capable of implementing the video acquisition function, such as cameras, sensors, etc. Users can use the video acquisition devices on the terminals 101, 102, 103 to acquire videos.

[0029] The server 105 can be a server that provides various services, such as a background server for processing data displayed on the terminal devices 101, 102, 103. The background server can analyze and process the received data, and can feedback the processing results (such as identification results) to the terminal devices.

[0030] It should be noted that the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers or as a single server. When the server is software, it can be implemented as multiple software or software modules (such as multiple software or software modules for providing distributed services), or as a single software or software module. No specific limitation is made here.

[0031] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. In particular, when the target data does not need to be obtained remotely, the above system architecture may not include a network and only include terminal devices or servers.

[0032] Figure 2 is a schematic flowchart of a method for identifying inefficient land use based on a pressure - state - response framework in an embodiment of the present application.

[0033] Please refer to Figure 2 , a method for identifying inefficient land use based on a pressure - state - response framework in an embodiment of the present application, is applied to a server. The method includes: S201. Obtain geographical location data, land use type data, socioeconomic data, population data, ecological environment data, infrastructure data, and regulatory response data of multiple target areas; In the process of identifying inefficiently utilized land, it is first necessary to obtain geographical location data, land use type data, socio-economic data, population data, ecological environment data, infrastructure data, and regulatory response data for multiple target areas to support subsequent spatial correlation analysis and evaluation of inefficiently utilized land. Among them, inefficiently utilized land refers to land types with low land use efficiency that fail to fully realize their due economic, social, or ecological value. It usually manifests as insufficient land use intensity, unreasonable spatial layout, serious resource waste, or restrictions on development and utilization. Inefficiently utilized land may include idle land, industrial land with low floor area ratio, old urban areas with declining functions, and agricultural land with low output. Its causes may involve industrial restructuring, changes in population flow, lagging infrastructure, or the impact of policy regulation. Identifying and optimizing inefficiently utilized land is of great significance for improving land use efficiency, promoting sustainable economic development, and optimizing the urban-rural spatial structure. Geographical location data includes latitude and longitude coordinates, administrative divisions, topographic features, etc., and is usually extracted from remote sensing images, GIS databases, and surveying and mapping data; land use type data covers categories such as cultivated land, forest land, and construction land, and can be obtained through land survey data, remote sensing image interpretation, and map databases; socio-economic data involves indicators such as regional GDP, fiscal revenue and expenditure, and industrial structure, and generally comes from statistical yearbooks, economic development reports, and enterprise operation data; population data includes total population, density, mobility, etc., and can be obtained from population statistics, mobile communication data, and social surveys; ecological environment data covers green space coverage rate, water resource utilization, pollution index, etc., and can be obtained using remote sensing data, environmental monitoring station data, and ecological assessment reports; infrastructure data involves transportation networks, municipal facilities, public service facilities, etc., and can be extracted through urban planning data, map service platforms, and traffic management data; regulatory response data is used to reflect the implementation effect of policies, including the implementation of land policies, the progress of plan implementation, land supply methods (such as transfer, lease), and the impact of industrial support policies on land use, and can be obtained from plan implementation reports, land use monitoring data, and industrial development evaluation documents.

[0034] S202. Calculate the spatial correlation coefficient between the first target area and the second target area based on the geographical location data, land use type data, socio-economic data, and ecological environment data to obtain a spatial correlation matrix; Among them, the first target area is any target area, and the second target area is any target area other than the first target area.

[0035] Determine the first central coordinate point of the first target area and the second central coordinate point of the second target area according to the geographical location data; calculate the geographical distance attenuation coefficient between the first target area and the second target area based on the spatial distance between the first central coordinate point and the second central coordinate point; construct a land use type conversion matrix based on the land use type data, and calculate the land use type similarity between the first target area and the second target area according to the land use type conversion matrix; calculate the social-ecological system coupling degree between the first target area and the second target area based on the social economic data and the ecological environment data; perform a weighted calculation on the geographical distance attenuation coefficient, the land use type similarity and the social-ecological system coupling degree to obtain the spatial correlation coefficient between the first target area and the second target area; construct a spatial correlation matrix according to the spatial correlation coefficient.

[0036] In step S202, first, determine the central coordinate points of the first target area and the second target area respectively according to the geographical location data. Specifically, the geometric center method or the weighted center method can be used for calculation, and the central point can be calculated by taking the average value of multiple geographical coordinate points within each area. For example, if a certain area contains multiple geographical units, the central point of the area can be determined by calculating the weighted average of the longitude and latitude of these geographical units. Finally, the central point of the first target area is called the first central coordinate point, and the central point of the second target area is called the second central coordinate point.

[0037] After determining the first central coordinate point and the second central coordinate point, the spatial distance between them needs to be calculated next. Usually, the Euclidean distance formula or the spherical distance formula (such as the Haversine formula) can be used to calculate the geographical distance between two points. Based on this spatial distance, the geographical distance attenuation coefficient can be further calculated. The geographical distance attenuation coefficient usually reflects the trend that the spatial interaction between two regions decreases as the distance increases. For example, a negative exponential function or a power function can be used to represent the attenuation relationship, such as D = e -αd , where D is the geographical distance attenuation coefficient, d is the spatial distance between the first central coordinate point and the second central coordinate point, and α is the attenuation parameter.

[0038] The land use type data reflects the land use data in the region, and the land use type conversion matrix is a matrix used to describe the conversion probability between different land use types. Each element of the land use type conversion matrix represents the probability of a certain land use type being converted into another land use type. In the land use type data, calculate the conversion area between each land use type according to the land use type distribution of two time periods (such as t0 and t1) in the historical land use type data. Let A ij represent the area that belongs to land use type i at time t0 and is converted into land use type j at time t1, then the conversion probability P ij is calculated as follows: Among them, P ijDenote the probability of land use type i converting to j; A ij Denote the area of land use type i changing to land use type j; Denote the total area of type i, that is, all the areas belonging to type i at time t0. By calculating the conversion probabilities between all land use types, a land use type conversion matrix is obtained.

[0039] After obtaining the conversion matrix, further calculate the land use type similarity L between two target regions. A common method is cosine similarity: Among them, and respectively represent the proportions of the first target region and the second target region in land use type i, and n is the number of land use types.

[0040] Furthermore, it is necessary to calculate the social-ecological system coupling degree C between two target regions based on social and economic data and ecological environment data. Social and economic data may include population density, GDP (Gross Domestic Product), industrial structure, etc., and ecological environment data may include vegetation coverage rate, water resource status, environmental pollution degree, etc. Calculate the social and economic system score S1 and ecological environment system score E1 of the first target region and the social and economic system score S2 and ecological environment system score E2 of the second target region respectively. Common methods include the entropy weight method or the principal component analysis method. Finally, the social-ecological system coupling degree C is calculated as follows: The calculation formula of the social-ecological system coupling degree ensures that the value of the social-ecological system coupling degree is between [0,1]. The larger the value, the closer the social and ecological systems of the two regions are.

[0041] After that, it is necessary to perform weighted calculations on the geographical distance attenuation coefficient, land use type similarity, and social-ecological system coupling degree to obtain the spatial correlation coefficient between the first target region and the second target region. The weighted calculation can adopt the linear weighting method, and the weights can be determined based on the principal component analysis, entropy weight method, or expert scoring method to ensure that the calculation results can accurately reflect the spatial correlation degree between regions.

[0042] Finally, based on the calculated spatial correlation coefficient, construct a spatial correlation matrix. The spatial correlation matrix is a symmetric matrix, where each element S ij represents the spatial correlation coefficient between the i-th region and the j-th region. The diagonal elements of the matrix are usually set to 1 (indicating that the spatial correlation degree of itself is the maximum value), and the values of the non-diagonal elements are obtained through the above calculations.

[0043] S203. Based on social and economic data, population data, ecological environment data, infrastructure data, regulatory response data, and the spatial correlation matrix, construct an evaluation index system for inefficient land use; Construct pressure layer indicators based on socioeconomic data and population data; construct state layer indicators based on ecological environment data and infrastructure data; construct response layer indicators based on regulatory response data; determine the first spatial weight of pressure layer indicators, the second spatial weight of state layer indicators, and the corresponding third spatial weight of response layer indicators based on the spatial correlation matrix; construct an evaluation index system for inefficient land use based on pressure layer indicators, state layer indicators, response layer indicators, and target spatial weights, where the target spatial weights include the first spatial weight, the second spatial weight, and the third spatial weight.

[0044] The pressure layer indicators are used to measure the socioeconomic and population pressures faced by regional land use, and their core is the comprehensive analysis based on socioeconomic data and population data. Socioeconomic data includes GDP, industrial structure, land use intensity, urbanization rate, etc., while population data includes population density, population growth rate, per capita construction land area, etc. First, standardize these indicators to make them comparable, such as using range standardization or Z-score standardization. Then, use weight determination methods (such as entropy weight method, analytic hierarchy process, etc.) to calculate the comprehensive scores of each indicator, and finally form the pressure layer indicators. The larger the value of the pressure layer indicators, the greater the socioeconomic and population pressures faced by the region, which may lead to a decline in land use efficiency or excessive resource consumption.

[0045] The state layer indicators are used to reflect the ecological environment and infrastructure conditions of the region to measure the current situation and sustainability of land use. Ecological environment data includes green space coverage rate, water resource utilization rate, air pollution index, soil quality, etc., while infrastructure data includes traffic accessibility, density of public service facilities, and perfection of municipal facilities. Similar to the pressure layer indicators, first standardize these data and determine the weights according to expert evaluation or data-driven methods (such as principal component analysis), and finally calculate the scores of the state layer indicators. The higher the value of the state layer indicators, the better the ecological environment quality and the more perfect the infrastructure in the region, which helps to improve land use efficiency and sustainable development ability.

[0046] The response layer indicators are used to measure the government's and society's regulation and governance capabilities for the problem of inefficient land use, and are mainly constructed based on regulatory response data. Regulatory response data includes the implementation of land policies, the number of land consolidation and reclamation projects, the implementation of environmental protection policies, the intensity of land use planning control, etc. First, normalize each piece of regulatory response data to make their numerical ranges consistent. Then, determine the indicator weights according to the evaluation of policy implementation effects or the expert scoring method, and calculate the scores of the response layer indicators. The higher the value of the response layer indicators, the stronger the policy response and implementation capabilities of the region in the governance of inefficient land use, which helps to reduce the problem of inefficient land use.

[0047] Spatial weights are used to measure the spatial correlation between regions, ensuring that the evaluation index system for inefficient land use can reflect the spatial distribution characteristics. Based on the spatial correlation matrix constructed in step S202, the first spatial weight of the pressure layer indicators, the second spatial weight of the state layer indicators, and the third spatial weight of the response layer indicators can be calculated respectively. The pressure layer indicators are used to measure the impact of social and economic development and population growth within a region on land use. However, these pressures are not only local but also affected by the surrounding regions. Therefore, we need to calculate the first spatial weight of the pressure layer based on the spatial correlation matrix to reflect the comprehensive influence of spatially adjacent regions. The calculation formula for the first spatial weight is: where, W P (i) is the first spatial weight of the pressure layer indicators; S ij is the spatial correlation coefficient between regions i and j in the spatial correlation matrix; P j is the pressure layer indicator of region j; N is the total number of target regions.

[0048] The state layer indicators are used to measure the ecological environment quality and infrastructure improvement degree of a region. However, these factors usually have a strong spatial diffusion effect. For example, a high-quality forest land or water source will not only improve the ecological environment of the region but also have a positive impact on the surrounding regions. Similarly, the construction of infrastructure such as roads, hospitals, and schools will also affect the accessibility and convenience of adjacent regions. Therefore, it is necessary to calculate the second spatial weight of the state layer based on the spatial correlation matrix to quantify these spatial diffusion effects. The calculation formula for the second spatial weight of the state layer is: where, W S (i) is the second spatial weight of the state layer; S ij is the spatial correlation coefficient between regions i and j in the spatial correlation matrix; S E (j) is the state layer indicator of region j; N is the total number of target regions.

[0049] The response layer indicators measure the government's and society's ability to regulate and manage the problem of inefficient land use. Such policy measures often have regional coordination. For example, a land consolidation project or environmental protection policy in a region may not only affect the local area but also affect the surrounding regions. Therefore, we need to calculate the third spatial weight of the response layer based on the spatial correlation matrix to reflect the coordination and spillover effects of policy implementation in adjacent regions. The calculation formula for the third spatial weight of the response layer is: where, W R (i) is the third spatial weight of the response layer; S ij is the spatial correlation coefficient between regions i and j in the spatial correlation matrix; R j is the response layer indicator of region j; N is the total number of target regions.

[0050] After calculating the spatial weights of the pressure layer, state layer, and response layer, it is necessary to comprehensively evaluate these weights to construct an evaluation index system for inefficient land use. The core of constructing the evaluation index system for inefficient land use lies in integrating the pressure layer indicators, state layer indicators, and response layer indicators, and combining their spatial influence weights to scientifically and reasonably evaluate the land use efficiency of each target area. To more accurately reflect the land use status of each region, the evaluation index system for inefficient land use uses a spatial weighting method to calculate the comprehensive evaluation index E L (i), and its calculation formula is as follows: E L (i) = α1·W P (i) + α2·W S (i) + α3·W R (i), where W P (i) is the first spatial weight; W S (i) is the second spatial weight; W R (i) is the third spatial weight; α1, α2, and α3 are the target spatial weights, indicating the relative importance of each layer of indicators to the comprehensive evaluation index, which can be determined by the entropy weight method, analytic hierarchy process, or expert scoring method, and satisfy α1 + α2 + α3 = 1.

[0051] S204. Determine the comprehensive evaluation index set of each target area through the evaluation index system for inefficient land use and land use type data; Determine the target index set of each target area through the evaluation index system for inefficient land use. The target index set includes a pressure index subset, a state index subset, and a response index subset; determine the first index set weight of the pressure index subset, the second index set weight of the state index subset, and the third index set weight corresponding to the response index subset according to the land use type data; obtain the comprehensive evaluation index set of each target area according to the target index set and the target index set weight. The target index set weight includes the first index set weight, the second index set weight, and the third index set weight.

[0052] In the evaluation index system for inefficient land use, it is first necessary to calculate the corresponding target index set for each target area. This target index set consists of three parts: a pressure index subset, a state index subset, and a response index subset. The pressure index subset mainly measures the impact of external factors on land use, such as population density, economic growth rate, construction land expansion rate, etc.; the state index subset reflects the current situation of land use within the region, including land use rate, building density, green space coverage rate, etc.; the response index subset evaluates the governance ability of the government or society to the problem of inefficient land use, such as the coverage rate of land consolidation projects, policy implementation, etc. By collecting relevant data for each target area and standardizing these data, the corresponding pressure index, state index, and response index can be calculated, thus forming a complete target index set. For example, in Area A of a certain city, the pressure index may be relatively high (such as rapid population growth), the state index may be medium (such as moderate land use rate), and the response index may be relatively low (such as insufficient policy support), which indicates that there may be a certain degree of inefficient land use in Area A, but there may still be room for optimization.

[0053] After the target index set is calculated, it is necessary to determine the weights of each index subset in combination with land use type data. The following steps can be included: extracting the land use type characteristics of each target area from the land use type data; constructing a land use type characteristic matrix for each target area based on the land use type characteristics; and determining the first index set weight, the second index set weight, and the third index set weight of the target index set in each target area according to the land use type characteristic matrix.

[0054] First, it is necessary to extract the land use type characteristics of each target area from the land use type data. Land use type data usually includes categories such as industrial land, commercial land, residential land, agricultural land, ecological land, etc. The proportion of each land use type is different in different regions, thus affecting the land use pattern of that region. For example, Area A of a certain city may consist of 40% industrial land, 30% residential land, 20% commercial land, and 10% ecological land, while Area B may be mainly composed of 50% residential land. These land use type characteristics will serve as an important basis for constructing a comprehensive evaluation index set in the following.

[0055] After extracting the land use type characteristics of each target area, it is necessary to construct a land use type characteristic matrix to quantify these characteristics and provide a data basis for subsequent calculations. The land use type characteristic matrix is usually represented by rows and columns, where the rows represent each target area and the columns represent different land use types. The values in the matrix represent the proportion of a certain target area in a specific land use type.

[0056] After the construction of the land use type feature matrix is completed, it is necessary to determine the weights of the target index set of the target index set based on this matrix, that is, the first index set weight (pressure index subset), the second index set weight (state index subset), and the third index set weight (response index subset). In a possible situation, the target index set weights are determined by combining data analysis and expert evaluation. First, according to the characteristics of different land use types, the relative importance of pressure, state, and response indices is initially set. For example, industrial land is greatly affected by economic development, so its pressure index weight is relatively high, while agricultural and ecological land pays more attention to land sustainability, so its state index weight is relatively high. Subsequently, using the entropy weight method or the principal component analysis method (PCA), based on the historical data of various land uses, calculate their contribution degrees to different index sets, and combine the Delphi method or the analytic hierarchy process (AHP), invite experts to evaluate and adjust the weights to ensure rationality. Finally, optimize the weights through the weighted average method or the multi-criteria decision-making method (such as TOPSIS) and normalize them to accurately reflect the role of different land use types in the identification of inefficient land use. Different types of land have different contributions to pressure, state, and response, so it is necessary to reasonably allocate weights. For example, industrial land is usually strongly affected by economic development and population growth, so its pressure index weight (the first index set weight) is relatively high; agricultural and ecological land pays more attention to the sustainability of land use, so its state index weight (the second index set weight) is relatively high; while government-planned land (such as affordable housing) is more dependent on policy regulation, so its response index weight (the third index set weight) is relatively high.

[0057] After determining the target index set and the corresponding weights, the comprehensive evaluation index set of each target area is obtained based on the target index set and the corresponding weights. The comprehensive evaluation index set is not just a single value, but a set containing multiple sub-indicators, which is used to comprehensively evaluate the land use efficiency. The comprehensive evaluation index set {E L (i)} consists of the following three subsets: Pressure index set {P i}: Reflects the pressure of land resource utilization in this area, such as population density, economic growth rate, construction land expansion rate, etc.; State index set {S i}: Reflects the current situation of land use in this area, such as land use rate, building density, green space coverage rate, etc.; Response index set {R i}: Evaluates the governance ability of the government or society to the problem of inefficient land use, such as the coverage rate of land consolidation projects, policy implementation, etc.

[0058] Finally, the comprehensive evaluation index set {E L (i)} consists of {P i , S i , R i}, and its corresponding weights {ω1, ω2, ω3} to form: {E L (i)} = {P i , S i , R i , ω1, ω2, ω3}.

[0059] S205. Determine the low - efficient land use level of each target area based on the comprehensive evaluation index set to obtain the low - efficient land use identification result.

[0060] In step S205, the low - efficient land use level is divided into four levels: first - level low - efficient, second - level low - efficient, third - level low - efficient, and fourth - level low - efficient. Among them, the first - level low - efficient has the highest level, indicating extremely low land use efficiency and requires priority rectification; the second - level low - efficient is the second, indicating relatively low land use efficiency and still having a large room for optimization; the third - level low - efficient reflects that although there are certain low - efficient situations in land use, the overall is relatively reasonable and only requires local adjustment; the fourth - level low - efficient indicates relatively high land use efficiency and does not require intervention. The classification basis of the low - efficient land use level is the calculation result of the comprehensive evaluation index E L (i). After obtaining the calculation result of the comprehensive evaluation index E L (i), the rule - setting method, data clustering analysis (such as K - means), entropy method or expert evaluation method is used to determine the specific threshold. For example, E L (i)>0.8 can be classified as the first - level low - efficient, 0.6 ≤ E L (i) ≤ 0.8 is classified as the second - level low - efficient, and so on.

[0061] After determining the classification standard of the low - efficient land use level, it is necessary to calculate the comprehensive evaluation index E L (i) for each target area, and determine the low - efficient land use level it belongs to according to the set threshold. For example, assume that the four areas A, B, C, and D in a certain city have E L (A)=0.85, E L (B)=0.72, E L (C)=0.55, E LIf (D) = 0.30, then Area A is classified as the first low - efficiency level, indicating serious land resource waste in this area, such as large - area idle land, low - floor - area - ratio development, etc., and land improvement or industrial upgrading should be prioritized; Area B belongs to the second low - efficiency level, indicating relatively low land - use efficiency, and there may be problems such as backward industrial structure and insufficient infrastructure. It is recommended to optimize the industrial layout or increase the land - development intensity; Area C belongs to the third low - efficiency level, indicating that the land use in this area is relatively reasonable, but there is still room for improvement, such as optimizing land use or improving surrounding supporting facilities; Area D belongs to the fourth low - efficiency level, indicating high land - use efficiency and no further intervention is required. Generally speaking, the areas of the first low - efficiency level need to be the key treatment targets, the areas of the second low - efficiency level should be appropriately optimized, the areas of the third low - efficiency level can be the second - priority adjustment targets, and the areas of the fourth low - efficiency level can remain unchanged.

[0062] In addition, to improve the accuracy of the classification of low - efficiency land grades, it is necessary to combine spatial - correlation analysis, that is, based on the spatial - correlation matrix of the target area, analyze the influence between adjacent areas. For example, if a certain area E L (i) itself is classified as the third low - efficiency level, but many of its surrounding adjacent areas belong to the first low - efficiency level, then it may be necessary to adjust the low - efficiency level of this area to reflect the spatial - agglomeration effect between regions. This can be measured by calculating the Moran's I index. When Moran's I>0, it indicates that there is an agglomeration effect of low - efficiency land in space and the grade should be appropriately adjusted; when Moran's I<0, it means that the low - efficiency land is discretely distributed and the original grade division can be maintained. When Moran's I = 0, the distribution of low - efficiency land is random and there is no obvious spatial - agglomeration or discrete trend. In this case, the classification of low - efficiency land grades mainly depends on the land - use characteristics of each area and does not need to be adjusted according to spatial location. After determining the low - efficiency land grades of all target areas, integrate the results to obtain the final low - efficiency land identification result.

[0063] Optionally, after step S205 of the embodiment shown in Figure 2 the following steps can be executed: Generate a spatial distribution map of low - efficiency land according to the low - efficiency land identification result and combined with geographical - location data; calculate the floor - area ratio and building density of each target area based on the comprehensive - evaluation index set and the spatial distribution map; evaluate the potential for improving land - use efficiency of each target area according to the floor - area ratio and building density; generate a land - use - efficiency improvement plan for each target area based on the potential for improving land - use efficiency.

[0064] Specifically, after the identification of inefficient land use is completed, a spatial distribution map of inefficient land use is generated in combination with the geographical location data of the target area. This distribution map is not only used to visually display the spatial distribution of different levels of inefficient land use, but also needs to provide data support for subsequent calculations of floor area ratio (FAR) and building density (BD). Therefore, this distribution map should include classification information of inefficient land use (such as first-level inefficiency, second-level inefficiency, etc.), and overlay building data (building height, building footprint area, number of building floors, etc.), current land use status (residential, commercial, industrial, etc.), planning control data (upper limit of floor area ratio, upper limit of building density, building height limit, etc.) and infrastructure data (road network, distribution of public facilities, etc.) to ensure the accuracy of subsequent calculations; in the data processing process, it is first necessary to obtain multi-source data such as remote sensing images, GIS databases, and 3D city models of the target area, integrate the multi-source data such as remote sensing images, GIS databases, and 3D city models, and then perform vectorization processing to extract building outlines and calculate the building footprint area. Then, through overlay analysis, the inefficient land use is integrated with building information and planning data, and spatial calculations are used to obtain the land area, total building area, and building density of each target area. At the same time, the inefficient land use levels are classified and visualized on the map, and information such as building height and floor area ratio is marked.

[0065] Based on the comprehensive evaluation index set and the spatial distribution map of inefficient land use, calculate the floor area ratio (FAR) and building density (BD) of each target area. This process first requires extracting key parameters such as the land area, total building area, and building footprint area of the target area. The formula for floor area ratio (FAR) is which represents the building development intensity of the area, and the formula for building density (BD) is which is used to measure the degree of surface coverage by buildings; in actual calculations, information such as the number of building floors, height, and floor area of buildings is obtained using data sources such as remote sensing images, 3D building models, and urban cadastral databases, and spatial analysis is carried out in combination with GIS technology.

[0066] After calculating the floor area ratio (FAR) and building density (BD) of each target area, it is necessary to evaluate the potential for improving the land use efficiency of the area by considering factors such as urban planning standards, industrial development needs, population density, and economic growth targets. That is, analyze whether there is room for optimized development or redevelopment in the area and determine possible improvement methods. Specifically, if the FAR of a certain area is much lower than the minimum control standard of the local urban planning, it indicates that there is a problem of too low building development intensity in the area, and the utilization efficiency can be improved by increasing the building height, optimizing the land use, encouraging mixed development, etc.; if the BD is too low, it means that the building coverage rate on the land is low, and there may be a large amount of idle land or inefficient buildings. The land use situation can be improved by increasing the building coverage rate, adding functional buildings, optimizing the land layout, etc.; in addition, it is also necessary to combine economic benefit analysis and social impact assessment to judge the feasibility of improving land use efficiency. For example, if the FAR of a certain area is 1.2, but the planning allows FAR = 3.0, it indicates that the area has great potential for improvement, and increasing the building height or introducing more efficient industries can be considered. If the FAR is already close to the upper limit, the room for improvement is limited, and other methods need to be used to optimize land use, such as adjusting the functional zoning or improving the infrastructure supporting facilities, to ensure the rational use and sustainable development of land resources.

[0067] Based on the potential for improving land use efficiency obtained from the assessment, it is necessary to formulate a land use efficiency improvement plan for each target area, clarify the improvement methods, optimization strategies, implementation steps and expected effects, and adopt differentiated optimization measures according to different types of inefficient land use. For example, for the first inefficient area (extremely inefficient, requiring priority remediation), land consolidation and redevelopment should be mainly implemented, such as demolishing inefficient buildings, introducing high-value-added industries, optimizing the layout of infrastructure, and increasing the land development intensity. For example, if the current FAR of a certain area is 0.8 and the planned FAR is 3.0, then comprehensive land development can be encouraged, and a commercial-residential mixed model can be introduced to improve the land use efficiency; for the second inefficient area (relatively inefficient, still requiring optimization), the land use efficiency can be improved through policy guidance, industrial upgrading, optimizing land use, etc. For example, the land use nature can be adjusted, changing a single residential area into a commercial-residential mixed area, and encouraging the efficient use of land resources; for the third inefficient area (generally inefficient, allowing partial optimization), local optimization strategies can be adopted for local inefficient problems, such as low building density and single function, such as increasing public facilities, improving traffic accessibility, and increasing the green space coverage rate to enhance the overall vitality of the area; while for the fourth inefficient area (not inefficient, no intervention required), the strategy of maintaining the status quo and strengthening management should be mainly adopted to ensure the sustainability of land use, and at the same time monitor the possible future inefficient trends; when formulating the improvement plan, factors such as policy support, investment cost, social impact, and environmental sustainability also need to be considered to ensure the feasibility and sustainability of the plan. For example, in high-density urban areas, the urban renewal model (such as "vertical development") can be encouraged, and in low-density rural areas, land compound use (such as the "agriculture + tourism" model) can be adopted to improve land use efficiency, and finally a comprehensive planning document is formed to provide a scientific basis for government planning, land remediation and urban development.

[0068] Optionally, Figure 2 A method for identifying inefficient land use based on the pressure-state-response framework shown also includes the following steps: Obtain the target comprehensive evaluation index set of the third target area, where the third target area is the target area with the identification result of inefficient land use, and the target comprehensive evaluation index set includes a target pressure index subset, a target state pressure index subset and a target response index subset; calculate the influence relationship coefficient between the target pressure index subset and the target state index subset; calculate the driving relationship coefficient between the target state index subset and the target response index subset; when the influence relationship coefficient and the driving relationship coefficient meet the preset warning conditions, trigger the warning mechanism.

[0069] Obtain the target comprehensive evaluation index set of the third target area, where the third target area is the target area with the identification result of inefficient land use, and the target comprehensive evaluation index set includes a target pressure index subset, a target state index subset and a target response index subset.

[0070] After obtaining the target comprehensive evaluation index set, it is necessary to calculate the influence relationship coefficient between the target pressure index subset and the target state index subset to evaluate the influence degree of external pressure factors on the current land use situation. This coefficient is usually calculated by correlation analysis, regression analysis or structural equation model (SEM). For example, the Pearson correlation coefficient is used to measure the linear correlation between the target pressure index subset and the target state index subset, or the target pressure index subset is estimated for the target state index subset through a regression model.

[0071] When calculating the driving relationship coefficient between the target state index subset and the target response index subset, the focus is on analyzing the influence degree of the social response measures on the land use situation. This coefficient can be calculated by causal inference methods, time series analysis or Granger causality test. Or use the Granger causality test to judge whether the target response index subset is the main driving factor for the change of the target state index subset. The calculated driving relationship coefficient can be used to evaluate the effectiveness of the current policy measures and provide a quantitative basis for subsequent strategies to optimize land use efficiency.

[0072] After calculating the influence relationship coefficient and the driving relationship coefficient, it is necessary to set preset warning conditions. When the two coefficients meet this condition, the warning mechanism is triggered. Specifically, the warning conditions can be set based on empirical thresholds, statistical analysis or machine learning models. For example, if the influence relationship coefficient exceeds 0.8 (that is, the influence of external pressure on the land use state is strong), and the driving relationship coefficient is lower than 0.2 (that is, the effect of the current policy measures on improving the land use situation is weak), it indicates that the problem of inefficient land use in this area may intensify, and intervention measures should be taken immediately. The warning mechanism can include automatically generating a warning report, sending a notice to the decision maker, initiating a special investigation or adjusting the land use optimization plan.

[0073] Please refer to Figure 3 FIG. An inefficient land use identification system 300 based on a pressure-state-response framework provided by an embodiment of the present application specifically includes: An acquisition module 301, configured to acquire geographical location data, land use type data, socio-economic data, population data, ecological environment data, infrastructure data and regulatory response data of multiple target regions; A construction module 303, configured to construct an evaluation index system for inefficient land use based on socioeconomic data, population data, ecological environment data, infrastructure data, regulatory response data, and a spatial association matrix; A determination module 304, configured to determine a comprehensive evaluation index set for each target area through the evaluation index system for inefficient land use and land use type data; An identification module 305, configured to determine the inefficient land use level of each target area based on the comprehensive evaluation index set, and obtain an inefficient land use identification result.

[0074] Optionally, a calculation module 302 is specifically configured to: Determine a first central coordinate point of a first target area and a second central coordinate point of a second target area according to geographical location data; calculate a geographical distance attenuation coefficient between the first target area and the second target area based on the spatial distance between the first central coordinate point and the second central coordinate point; construct a land use type conversion matrix based on the land use type data, and calculate the land use type similarity between the first target area and the second target area according to the land use type conversion matrix; calculate the social-ecological system coupling degree between the first target area and the second target area based on the socioeconomic data and the ecological environment data; perform weighted calculation on the geographical distance attenuation coefficient, the land use type similarity, and the social-ecological system coupling degree to obtain a spatial correlation coefficient between the first target area and the second target area; construct a spatial association matrix according to the spatial correlation coefficient.

[0075] Optionally, the construction module 303 is specifically configured to: Construct pressure layer indicators based on socioeconomic data and population data; construct state layer indicators based on ecological environment data and infrastructure data; construct response layer indicators based on regulatory response data; determine a first spatial weight of the pressure layer indicators, a second spatial weight of the state layer indicators, and a third spatial weight corresponding to the response layer indicators based on the spatial association matrix; construct an evaluation index system for inefficient land use based on the pressure layer indicators, the state layer indicators, the response layer indicators, and the target spatial weights, where the target spatial weights include the first spatial weight, the second spatial weight, and the third spatial weight.

[0076] Optionally, the determination module 304 is specifically configured to: Determine a target index set for each target area through the evaluation index system for inefficient land use, where the target index set includes a pressure index subset, a state index subset, and a response index subset; determine a first index set weight of the pressure index subset, a second index set weight of the state index subset, and a third index set weight corresponding to the response index subset according to the land use type data; obtain a comprehensive evaluation index set for each target area according to the target index set and the target index set weights, where the target index set weights include the first index set weight, the second index set weight, and the third index set weight.

[0077] Optionally, the determination module 304 is further specifically configured to: Extract the land use type features of each target area from the land use type data; construct a land use type feature matrix for each target area based on the land use type features; determine the first index set weight, the second index set weight, and the third index set weight of the target index set in each target area according to the land use type feature matrix.

[0078] Optionally, the system further includes an optimization module 306, which is specifically configured to: Generate a spatial distribution map of inefficient land use based on the inefficient land use identification result and the geographical location data; calculate the plot ratio and building density of each target area based on the comprehensive evaluation index set and the spatial distribution map; evaluate the potential for improving the land use efficiency of each target area according to the plot ratio and building density; generate a land use efficiency improvement plan for each target area based on the potential for improving the land use efficiency.

[0079] Optionally, the system further includes a warning module 307, which is specifically configured to: Obtain the target comprehensive evaluation index set of the third target area, where the third target area is a target area with an inefficient land use identification result, and the target comprehensive evaluation index set includes a target pressure index subset, a target state pressure index subset, and a target response index subset; calculate the influence relationship coefficient between the target pressure index subset and the target state index subset; calculate the driving relationship coefficient between the target state index subset and the target response index subset; when the influence relationship coefficient and the driving relationship coefficient meet the preset warning conditions, trigger the warning mechanism.

[0080] It should be noted that: when the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0081] This embodiment also discloses an electronic device. Refer to Figure 4 , the electronic device may include: at least one processor 401, at least one communication bus 402, a user interface 403, a network interface 404, and at least one memory 405.

[0082] Among them, the communication bus 402 is used to realize the connection and communication between these components.

[0083] Among them, the user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may further include a standard wired interface and a wireless interface.

[0084] Among them, the network interface 404 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).

[0085] Among them, the processor 401 may include one or more processing cores. The processor 401 connects various parts within the entire server through various interfaces and lines, and executes various functions of the server and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and by calling data stored in the memory 405. Optionally, the processor 401 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 401 may integrate one or a combination of several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 401 and may be implemented separately by a single chip.

[0086] Among them, the memory 405 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 405 may also be at least one storage device located far from the aforementioned processor 401. Such as Figure 4As shown in the figure, the memory 405, which is a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program of a method for inefficient land use identification based on the Pressure-State-Response framework.

[0087] In Figure 4 In the electronic device shown in the figure, the user interface 403 is mainly used to provide an interface for the user to input data and obtain the data input by the user; while the processor 401 can be used to call the application program of the method for inefficient land use identification based on the Pressure-State-Response framework stored in the memory 405. When executed by one or more processors 401, the electronic device is caused to execute the method as described in one or more of the above embodiments.

[0088] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0089] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] In the several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect couplings or communication connections of the device or unit can be in an electrical or other form.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 405 and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned memory 405 includes various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0094] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation manners of the present disclosure after considering the disclosure of the specification. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An inefficient land use identification method based on the pressure-state-response framework, characterized in that Applied to a server, the method includes: Obtain geographical location data, land use type data, socio-economic data, population data, ecological environment data, infrastructure data, and regulatory response data for multiple target areas; Calculate the spatial correlation coefficient between a first target area and a second target area based on the geographical location data, the land use type data, the socio-economic data, and the ecological environment data to obtain a spatial correlation matrix, where the first target area is any one of the target areas, and the second target area is any one of the target areas other than the first target area; Construct an evaluation index system for inefficient land use based on the socio-economic data, the population data, the ecological environment data, the infrastructure data, the regulatory response data, and the spatial correlation matrix; Determine the comprehensive evaluation index set for each target area through the evaluation index system for inefficient land use and the land use type data; Determine the inefficient land use level for each target area based on the comprehensive evaluation index set to obtain the inefficient land use identification result.

2. The method according to claim 1, wherein The calculating the spatial correlation coefficient between the first target area and the second target area based on the geographical location data, the land use type data, the socio-economic data, and the ecological environment data to obtain a spatial correlation matrix specifically includes: Determine the first central coordinate point of the first target area and the second central coordinate point of the second target area according to the geographical location data; Calculate the geographical distance attenuation coefficient between the first target area and the second target area based on the spatial distance between the first central coordinate point and the second central coordinate point; Construct a land use type conversion matrix based on the land use type data and calculate the land use type similarity between the first target area and the second target area according to the land use type conversion matrix; Calculate the socio-ecological system coupling degree between the first target area and the second target area based on the socio-economic data and the ecological environment data; Perform weighted calculation on the geographical distance attenuation coefficient, the land use type similarity, and the socio-ecological system coupling degree to obtain the spatial correlation coefficient between the first target area and the second target area; Construct a spatial correlation matrix according to the spatial correlation coefficient.

3. The method according to claim 1, wherein The constructing an evaluation index system for inefficient land use based on the socio-economic data, the population data, the ecological environment data, the infrastructure data, the regulatory response data, and the spatial correlation matrix specifically includes: Construct pressure layer indicators based on the socio-economic data and the population data; Construct state layer indicators based on the ecological environment data and the infrastructure data; Construct response layer indicators based on the regulatory response data; Determine the first spatial weight of the pressure layer indicators, the second spatial weight of the state layer indicators, and the third spatial weight corresponding to the response layer indicators based on the spatial correlation matrix; Construct an evaluation index system for inefficient land use based on the pressure layer index, the state layer index, the response layer index, and the target space weight, where the target space weight includes the first space weight, the second space weight, and the third space weight.

4. The method according to claim 1, wherein Determine the comprehensive evaluation index set of each target area through the inefficient land use evaluation index system and the land use type data, specifically including: Determine the target index set of each target area through the inefficient land use evaluation index system, where the target index set includes a pressure index subset, a state index subset, and a response index subset; Determine the first index set weight of the pressure index subset, the second index set weight of the state index subset, and the third index set weight corresponding to the response index subset according to the land use type data; Obtain the comprehensive evaluation index set of each target area according to the target index set and the target index set weight, where the target index set weight includes the first index set weight, the second index set weight, and the third index set weight.

5. The method according to claim 4, wherein The step of determining the first index set weight of the pressure index subset, the second index set weight of the state index subset, and the third index set weight corresponding to the response index subset according to the land use type data specifically includes: Extract the land use type characteristics of each target area from the land use type data; Construct a land use type characteristic matrix for each target area based on the land use type characteristics; Determine the first index set weight, the second index set weight, and the third index set weight of the target index set in each target area according to the land use type characteristic matrix.

6. The method according to claim 1, characterized in that, After determining the inefficient land use level of each target area based on the comprehensive evaluation index set to obtain the inefficient land use identification result, the method further includes: Generate a spatial distribution map of inefficient land use according to the inefficient land use identification result in combination with the geographical location data; Calculate the plot ratio and building density of each target area based on the comprehensive evaluation index set and the spatial distribution map; Evaluate the potential for improving land use efficiency of each target area according to the plot ratio and the building density; Generate a land use efficiency improvement plan for each target area based on the potential for improving land use efficiency.

7. The method according to claim 1, the method further includes: Obtain the target comprehensive evaluation index set of the third target area, where the third target area is the target area with the inefficient land use identification result being inefficient land use, and the target comprehensive evaluation index set includes a target pressure index subset, a target state pressure index subset, and a target response index subset; Calculate the influence relationship coefficient between the target pressure index subset and the target state index subset; Calculate the driving relationship coefficient between the target state index subset and the target response index subset; When the influence relationship coefficient and the driving relationship coefficient meet the preset warning conditions, trigger the warning mechanism.

8. An inefficient land use identification system based on the pressure-state-response framework, characterized in that Including: An acquisition module for acquiring geographical location data, land use type data, socio-economic data, population data, ecological environment data, infrastructure data, and regulatory response data of multiple target areas; A calculation module, configured to calculate a spatial correlation coefficient between a first target area and a second target area based on the geographical location data, the land use type data, the socioeconomic data, and the ecological environment data, so as to obtain a spatial correlation matrix, where the first target area is any one of the target areas, and the second target area is any one of the target areas other than the first target area; A construction module, configured to construct an evaluation index system for inefficient land use based on the socioeconomic data, the population data, the ecological environment data, the infrastructure data, the regulation and response data, and the spatial correlation matrix; A determination module, configured to determine a comprehensive evaluation index set of each target area through the evaluation index system for inefficient land use and the land use type data; An identification module, configured to determine the inefficient land use level of each target area based on the comprehensive evaluation index set, so as to obtain an inefficient land use identification result.

9. An electronic device, characterized in that, Comprising: One or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, where the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the electronic device to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the electronic device, the electronic device is caused to execute the method according to any one of claims 1-7.

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